Community Participation, Teacher Effort, and Educational
Outcome: The Case of El Salvador’s EDUCO Program
By: Yasuyuki Sawada
Working Paper Number 307
November 1999

Community Participation, Teacher Effort, and Educational Outcome:
The Case of El Salvador’s EDUCO Program*by

Yasuyuki Sawada**
Revised May 2000

Abstract
Based on a principal-agent model, this paper investigates the organizational structure that made the El
Salvador's primary school decentralization program (EDUCO program) successful. First, we employ the“augmented” reduced form educational production function by incorporating parents and community
involvement as a major organizational input. We observe consistently positive and statisticallysignificant EDUCO participation effects on standardized test scores. Then we estimated teacher
compensation function, teacher effort functions, and input demand functions by utilizing the theoretical
implications of a principal (parental association)-agent (teacher) framework. While the EDUCO school
teachers receive piece rate, depending on their performance, wage payment is relatively fixed in the
traditional schools. Empirical results indicate that the slope of wage equation is positively affected by the
degree of community participation. This finding can be interpreted as the optimal intensity of incentive.
Hence, teacher’s effort level in the traditional schools is consistently lower than that in the EDUCO
schools, indicating a moral hazard problem. Community participation through parental group's classroom
visits seems to enhance the teacher effort level and thus increases students’ academic performance
indirectly. Parental associations can affect not only teacher effort and their performance by imposing anappropriate incentive scheme but also school-level inputs by decentralized school management. Our
empirical results support the view that decentralization of education system should involve delegation ofschool administration and teacher management to the community group.
Keywords: Economic analysis of social sector reform; the optimal intensity of incentive condition; moral
hazard; education production function; fixed effects instrumental variable estimation.
JEL classification: D82; I2; O12; O54* I would like to thank Manny Jimenez for inspiring me to initiate this paper. This paper is a product of
the research project “Impact Evaluation of Education Projects Involving Decentralization andPrivatization (http://www.worldbank.org/research/projects/impact.htm),” led by Elizabeth King at theDevelopment Research Group of the World Bank. The updated version of this paper will be available atthe homepage of the project. The comments and suggestions from Takeshi Amemiya, Marcel Fafchamps,
Yujiro Hayami, Gary Jefferson, Anjini Kochar, Takashi Kurosaki, Lawrence Lau, Lee Lillard, Ronald
McKinnon, Robert McMillan, Jonathan Morduch, Albert Park, Keijiro Otsuka, Hideo Owan, Rohini
Somanathan, Jan Svejnar, Katherine Terrell, Pan Yotopoulos and seminar participants at Universities ofMichigan, Stanford, Tokyo, and Yokohama National, and the 1999 Japanese Economic Association
Annual Meeting are gratefully acknowledged. The findings and conclusions are the author’s and should
not be attributed to the World Bank, its Board of Directors, or any of its member countries.
** Development Research Group, the World Bank and Department of Advanced Social and International
Studies, University of Tokyo, Komaba. Email address: sawada@stanfordalumni.org

1William Davidson Institute Working Paper Number 307

1 Introduction

Recently, social sector amelioration has become the focal point of micro-level policy reforms indeveloping countries. Since social services such as education, nutrition and health are directly related to
improvement of households' welfare, it is important to investigate the conditions that are necessary toenhance local public action to improve the well-being of the poor. Examples of intra-community
cooperation include issues of forming irrigation and water user associations, providing communal healthfacilities, and enhancing community involvement in schools. The central question of social sector reform,
therefore, is how rural communities in developing countries form associations to overcome weak formal
institutions such as the lack of enforcement mechanism for formal contracts and various information
problems.
Individuals in a community, however, are diversified with respect to their ability to cooperate information of community groups, local social service provision, and monitoring public agencies andofficials. As a result, traditionally, the resource allocation process in developing countries’ social sectors
has been characterized by a highly centralized structure. In an education system, which is our focus,
school administrators, teachers, students and parents traditionally play only a limited role in determining
school resource allocation. Since monitoring, inspecting and enforcing detailed guidelines for individual
schools are likely to be very costly, an education ministry sets rules, such as for the distributions ofbudgetary allocations between teachers’ salaries and other inputs. Teachers are usually government
employees and subject to highly regulated and hierarchical management systems. Teacher salaries areoften fixed or negotiated centrally. If the rules set by the government do not match the school’s needs orthe community’s preferences, the use of resources becomes inefficient. Moreover, school administrators
might become accountable not to parents and students but to central authorities, and thus a centralized
system will lead to low school productivity. The widespread observations of failures of centrally planned
education systems, especially in developing countries, indicate that community specific factors andconditions should be considered properly for effective provision of social sector resources [World Bank
(1996a)].
With this regard, “community participation” has recently become a key concept in local-level
decision making process in the arguments on the social sector reforms. The stress on the process andmechanism design of participation is a natural outgrowth of agency problems. In the context ofdeveloping countries, the policy makers and researchers began to recognize the significance of potential
discrepancies between the actions taken by an agent and the interests of those that the agent is supposed
to serve [Stiglitz (1999)]. Recent research provides evidence that participation is necessary for an1William Davidson Institute Working Paper Number 307

effective development of a community. Isham, Narayan, and Printchett (1995), for example, have shownthat increasing beneficiary participation directly causes better project outcomes, by using data from 121
diverse rural water projects. Participation not only brings to the project relevant information that outsidedevelopment agencies are not likely to have, but also brings with it commitment. In turn, commitment
brings with it greater effort of agents that is a necessary condition of a successful project [Stiglitz (1995)].
Local participation in project implementation ensures that projects and policies match with the local
context. Educational quality, for example, can be improved when schools are able to implement resource
allocation according to local conditions and are accountable to parents and communities throughcommunity participation [World Bank (1995)]. In the context of developing countries, two examples of
empirical investigations are James, King and Suryadi (1996) for Indonesia, Jimenez and Paqueo (1996)
for the Philippines, and Glewwe (1997) for the Philippines. These studies conclude that there are
efficiency gains from community-based involvement. However, in spite of the powerful reasoning, there
is relatively little empirical evidence except the above mentioned studies to document the merits ofcommunity-management in education, especially in the context of developing countries. The main reasonof the lack in empirical studies is that these administrative arrangements have only recently begun to be
implemented in developing countries [World Bank (1996a)].
As another important background of our research, the recent charter school and school-based
management movements in the US also document the importance of community participation and school
autonomy in education [Hanushek (1994)].1 Charter schools are independent public schools designed andoperated by educators, parents, community leaders, and others. By construction, the charter schools are
more responsive to communities and free from the demands of the bureaucratic process. The autonomy
of charter schools overcomes the traditional bureaucracy and regulations that prevent an efficient
allocation of school resources. Yet, we should note that well-defined goals and performance incentives
are the key of a successful school-based management system [Hanushek (1994)]..
This paper investigates the role of community participation in the area of primary education
reform through a new case study from El Salvador. We examine a recent successful example of El
Salvador’s Community-Managed Schools Program (more popularly known by the acronym, EDUCO, or
Educacion con Participacion de la Comunidad). The EDUCO program is an innovative program for bothpre-primary and primary education to decentralize education by strengthening direct involvement andparticipation of parents and community groups [See, for example, Jimenez and Sawada (1999)]. In termsof both theoretical and empirical analyses, we will investigate the incentive and organizational structuresthat make the EDUCO program overcome the inefficiency that is widely observed in the centralized
1

As of February 1999, 34 states and the District of Columbia had passed charter school laws and 27 states and the2William Davidson Institute Working Paper Number 307

education systems.
There are two important empirical findings emerge. First, we observe consistently positive andstatistically significant community participation effects on standardized test scores. Second, empiricalresults of teacher compensation scheme estimation indicate that the slope of wage equation is positively
affected by the degree of community participation. As a result, teachers’ effort levels in the traditional
centrally-managed schools are consistently lower than that in the community-managed EDUCO schools,
indicating a moral hazard problem. Peer pressure through community participation seems to enhance theteacher effort level and thus increases students’ academic performance indirectly. Our empirical results
support the view that decentralization of education system should involve delegation of school
administration and teacher management to the community group.

The EDUCO Program in El Salvador

In El Salvador, community education has endogenously emerged in the 1980s during the civil
war. At that time, public schools could not be extended because of warfare around the country. Some
communities, therefore, took the initiative to organize their own schools, administered and financially
supported by an association of households. In 1991, El Salvador’s Ministry of Education (MINED)
institutionalized these community schools as the EDUCO schools and also decided to use the prototype as
the principal method of expanding education in rural areas. In EDUCO schools, the Community
Education Association (Asociacion Comunal para la Educacion or ACE) elected from the parents of thestudents has a central role of school administration and management: ACE is responsible for allocation ofschool budgets and for hiring and dismissing teachers by monitoring teacher’s performance. The
partnership between MINED and ACEs is expected to improve school administration and management byreflecting local demand needs more appropriately. On the other hand, the parents’ associations (Sociedad
de Padres de Familia or SdPF) in traditional schools have limited roles: SdPF has no administrative
capability over school personnel nor budget.
Initially, MINED imposed three purposes of the EDUCO program; first, augmentation of
educational supply in the poorest rural communities; second, promotion of the participation of local
community members in education; third, improvement of the quality of pre-primary and primaryschooling. The initial evidence indicates that the EDUCO program has accomplished these objectives
successfully [El Salvador, MINED (1995)]. MINED also expects that moving away from traditional
programs that provide education centrally could improve outcomes through increased community andDistrict of Columbia are operating 1,200 charter schools, serving over 300,000 students.
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parent involvement.

Outline of the Paper

This paper investigates the organizational structure and incentive scheme which made the
EDUCO program successful. Figure 1 and 2 represent the basic school governance structure inside theaugmented production function for traditional and EDUCO schools, respectively. First, we will construct
a formal theoretical model of the EDUCO school. Utilizing a principal-agent framework, we will showthat parents or parental associations (principal) can affect not only teacher (agent) effort and theirperformance by imposing an appropriate incentive scheme but also school-level inputs by delegatedschool management.

Outputs of educational production, usually measured by child educational

performance, are created by school level inputs as a result of school-community interactions and byhousehold level inputs such as home teaching (Figure 1 and 2).
Secondly, this paper examines empirically the prediction of the formal model. It estimates“augmented” reduced form production functions using two measures of educational outcomes, i.e.,
standardized test scores in mathematics and language, among third-grade students.2 We incorporate
parents and community involvement as major organizational inputs. Then, the transmission mechanism
from community participation to educational output is investigated. To this end, we estimate teacher
compensation function, teacher effort function, and input demand functions, based on the theoretical
implications of the principal-agent framework. Empirical results indicate that the slope of wage equationis positively affected by the degree of community participation and teachers’ effort levels in the
traditional centrally-managed schools are consistently lower than that in the community-managed
EDUCO schools, indicating a moral hazard problem.
The rest of this paper is organized as follows.

Section 2 discusses the importance of an

appropriate teacher payment scheme is reviewed. In Section 3, the formal theoretical framework is
derived. We first derive the optimal incentive scheme by solving an optimization problem of parental
association. Then the optimal level of teacher effort is determined endogenously. The final sub-section
represents the “augmented” reduced form educational production function by taking into accountendogenous teacher effort as a function of community participation. In Section 4, we discusse the data
set and the estimation results of the student achievement equation. Section 4 also present the formal
econometric framework. First, it includes of the production function is constructed and estimated.
2

Jimenez and Sawada (1999) estimates the reduced form production function extensively. The section of theestimation of production function in this paper basically replicates the results of Jimenez and Sawada (1999) with analternative set of variables.
4William Davidson Institute Working Paper Number 307

Second, the paper empirically investigates the theoretical prediction of the transmission mechanisms from
community participation to educational outcome. We estimate teacher compensation function, teacher
effort functions, and input demand functions. Section 5 concludes with discussions of policy implications.

2 The Importance of Teacher Payment Scheme and Community Participation

In a conventional centralized education system, teacher payment rates are usually set by thecentral government. While in most developed countries, teachers’ organizations negotiate levels of
payments, their influence varies depending on the collective bargaining process and the form ofnegotiation. In developing countries, government regulations usually construct the teacher payment
scheme. For example, in Paraguay, a completely fixed-wage compensation scheme is employed forteacher payments [World Bank (1996b)]. There are no wage premiums for seniority and no rewards forsuperior performance. Such a centralized education system with rigid teacher payment schemes is highly
likely to create deterioration of teacher effort and educational quality. The prototypical argument is thatpayments that are not connected to performance discourage high productivity [Cohn (1996)]. Teacher
compensation, therefore, must be linked to job performance in order to retain well-qualified teachers[Gaynor (1998)]. It is reasonable to expect that linking compensation to job performance would motivate
teacher and thus improve quality of education. In recent years, merit teacher payment schemes have been
introduced in many states and districts in the US [Cohn (1996)].3 A study of US schools by Jacobson(1988), for example, concluded that the implementation of an incentive plan that monetarily rewarded
high rates of teacher attendance was accompanied by a significant, short-term reduction in teacher
absences. During the first year of this attendance incentive plan, teacher absences declined significantly
and the total number of teachers with perfect attendance increased four-fold. Cohn (1996, p.227) also
shows that the South Carolina Teacher Incentive Plan Award had a significant impact on scores of student
achievements. Mickler (1987) concludes that three merit pay plans conducted in Ladue School District,MO, Dalton County, GA, and Lake Forest, IL have been successful as well. Mickler (1987) argues thatan important ingredient in a successful implementation of merit payment scheme is the active
participation and involvement of teachers in the teaching performance evaluation process.
The empirical evidence, however, is not abundant enough to endorse the importance of merit
payment in developing countries. But the following examples from South America and South Asia are
the exceptions. In the first example, a locally monitored incentive scheme has been implemented in
Nicaragua [Gaynor (1998)]. The Nicaraguan Ministry of Education is developing guidelines that include
3

The merit-based payment is defined as the payment by results [Cohn (1996, p.210)].
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William Davidson Institute Working Paper Number 307

teacher performance incentives, personnel management, and criteria for applying salary supplements toteachers funded by local contributions. Approximately, 25% of the basic teachers' salary are paid
monthly as teacher incentives and they are adjusted each term according to teacher performance. This
payment is based on regular teacher attendance and low student dropout rates. The school council elects
a member who monitors teachers' attendance and administers the local contributions that are received
directly by the school. Teacher attendance reports are signed weekly by the chair of the school council
and then sent to the municipal education council. Penalties apply when a teacher missed days of schoolor is late for school by 30 minutes or more on three occasions. The second example is the teacher
payment reform in the Dominican Republic. With help from the World Bank, the Dominican government
has fabricated a new teacher compensation system with the aim of developing a clearer carrier structure
for teachers [Gaynor (1998)]. Under the new system, teacher salaries increase according to
improvements in their classroom performance. Performance evaluation of teacher accounts for 20-25%
of total salaries. The final example is a merit payment scheme, which has been successfully implemented
even in one-teacher schools in Nepal. At schools in the Seti Zone Project in Nepal, teachers in oneteacher schools work under the performance-based payment scheme. Teachers in these schools receive
payments directly from supervisors but only if they attained observable performance goals [Bennett(1991)].
2.1 Potential Pitfalls of the Merit-based Payment Scheme

Under the above mentioned merit payment systems, a significant portion of each teacher's salaryis based on her/his performance evaluated by supervisors. The opponents of the merit-based systemconclude that research on teacher compensation and reward preferences in US history provides only
limited support for the proposition that the merit-based payment system can effectively motivate teachers
to improve their performance [Cohn (1996)]. Indeed, there is a wide range of technical, organizational,
and financial obstacles for merit-based payment schemes. First, performance-based payments might
encourage “opportunistic” behavior, i.e., behavior that benefits the individual teacher at the expense ofothers [Cohn (1996)]. Among teachers, cooperation becomes rare, while competition is common.
Opportunistic behavior by teachers, jealousy by those not receiving the merit bonus, and effect of merit
pay on administrators may have strong negative effects on the school quality.
Second, it is difficult to establish objective and fair criteria of teacher performance. Before
standardized tests were common, school administrators usually employed their own “merit ratings” todetermine teacher payments, opening the door to nepotism, arbitrariness, and discrimination, causing
serious morale problems. Even if test scores become available, the use of student achievement data may
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be problematic. Teachers who are compensated based on test scores of students would be tempted toneglect other aspects of education such as social skills and creative thinking. Teachers might also be led
to teaching the most receptive students, whose test scores are easiest to improve, while neglecting
students who have more trouble learning [Milgrom and Roberts (1992, p.230)].
Objective indicators of teaching performance, therefore, are difficult to generate, but they arenecessary for an impartial and effective merit-pay system. As a result, the predominant payment
mechanism for teachers in the United States is the single salary schedule [Cohn (1996)]. A study
concluded that the popularity of the US's merit teacher payment scheme in the 1920's and 1950's resulted
in widespread failure in the education program [Johnson (1984)]. Failure of the plans to meet theirobjectives, funding shortages and overall expense of the programs, and recognition that the merit pay
bonuses did not provide sufficient incentives to teachers became problematic [Johnson (1984)].
In fact, similar problems with the merit-based teacher remuneration scheme apply to developing
countries [Lockheed, Verspoor and associates (1991)]. First, monitoring by supervisors incurs various
monetary and non-monetary costs. Usually teacher supervisors face difficulty when monitoring aconsistent set of activities, since establishing and implementing an objective teacher evaluation systemthat justifies the supervisor's decision is costly. Especially in small rural schools with only one or twoteachers, teacher evaluation by external supervisors becomes expensive and thus proper evaluation may
be inherently difficult. Nonetheless, fair and appropriate evaluation is the fundamental requirement foran effective merit-based payment scheme. Second, potential inconsistency between true and observable
teacher efforts can create serious inefficiency in the system. Under the merit payment scheme withexternal supervisors, teachers become accountable not to parents and students but to the supervisors. The
scheme might promote uncooperative teacher attitudes toward parents and students. As a result, systemoutcomes will become inconsistent with welfare objective of the community.

2.2 Community Participation as a Necessary Condition

However, teacher monitoring by communities, not by external supervisors, can overcome these
inherent problems of a merit-based teacher payment scheme. Close monitoring by community groups canbe less costly than teacher evaluations by external supervisors. Even in a small rural school with only one
teacher, frequent and close teacher monitoring becomes possible if the supervisor is drawn from the samecommunity. When members of the community association are elected from the parents of the students,
inconsistency between the behavior of teachers and the welfare objectives of community disappear.
Teachers will become accountable to the community association which monitors, supervises, and7

William Davidson Institute Working Paper Number 307

evaluates their performance. As a result, teacher will become responsible to parents and students.
Community participation not only utilizes relevant information that outside government agencies are not
likely to have, but also imposes commitment on teachers, which leads them to exert greater effort.4
These advantages of community management and administration of schools seem to apply to the
El Salvador's EDUCO program. Teachers in EDUCO schools are selected, hired, supervised anddismissed by the parent associations drawn from the parents of the students (ACEs). The quality of
EDUCO teachers is judged according to their teaching performance and behavior. While an EDUCO
teacher’s salary is higher in many cases, they have less job stability than teachers in traditional schools,
especially when teachers do not put much effort towards improving their teaching quality [World Bank
(1995)]. As we will discuss in the later sections, the observed better attendance, performance andcommitment of EDUCO teachers may confirm the importance of improving the incentive structure basedon teacher performance evaluation under the community monitoring system.

3 The Theoretical Framework

The success of community participation depends on how it can overcome agency problems whicharise from discrepancies between the agent’s action and the principal’s objective. In our framework, the"principal" is either MINED in traditional schools or parental association in EDUCO schools, and the"agent" is a teacher. To investigate these teacher incentive issues formally, we will construct a principalagent model of endogenous teacher effort.
This paper makes two contributions to the principal-agent literature.5 First, there have been
4

This advantage of community participation is pointed by Stiglitz (1999).
The literature on theoretical treatments of principal-agent problem is substantial. For example, the model ofsharecropping, which was developed during the 1970’s in order to explain the particular agrarian institution indeveloping countries, became one of the first fully worked out principal-agent models in economics [Otsuka, Chuma,
and Hayami (1992)]. On the other hand, empirical studies of agency relationships are not widely available. For
example, despite the theoretical importance of moral hazard and the distinction between worker time and worker
effort, there is little direct empirical evidence on worker shirking. The lack of wide empirical studies can be
attributed to the difficulty for an econometrician to observe agent’s performances, e.g., employer’s estimate of worker
effort [Salanie (1998)]. While there have been wide studies of the disincentive effects of input use such as fertilizer,
seeds and worker time [Shaban (1985)], in the context of developing countries, there is almost no evidence on thewithholding of worker effort except Foster and Rosenzweig (1994) and Laffont and Matoussi (1995). Laffont andMatoussi (1995) estimated production functions using the tenant’s production as a natural measure of a worker’s
effort and performance. The data from Tunisia indicates that the tenant’s share of output and the degree of efficiency
have a positive relationship. Foster and Rosenzweig (1994) astutely used panel data on workers’ health and calorie
intake to measure the effort effects, rather than output effects, of different labor payment schemes. They showed theeffort level under piece rates is higher than the level under fixed salaries. These empirical studies of the basic agency
model show significant effects of compensation on performance. While it is important to point out that incentives
matter, these studies do not truly test predictions of the agency theory. A more precise test of agency theory is toaddress whether contracts are structured to reflect agency concerns [Prendergast (1999)]. Therefore, a second line of58William Davidson Institute Working Paper Number 307

almost no previous applications of the agency framework to social sector management issues in thecontext of developing countries. In the context of the United States, McMillan (1998) formulated an
insightful general equilibrium model of the interaction of parental and school preferences in determining
educational outcomes. He also implemented rigorous empirical investigations. On the other hand, thispaper formulates a formal theory and empirical framework of decentralization of education program in adeveloping country. Moreover, this paper empirically investigates not only the relationship between
compensation scheme and agent’s performance but also the consistency between the contracts and thetheoretical prediction. Therefore, this paper provides an accurate test of the agency theory by
investigating whether contracts are structured to reflect agency problems.
3.1 The Model of Endogenous Teacher Effort in El Salvador6

For the initial step of modeling our idea, a teacher's optimization problem can be represented as
an expected utility maximization. A teacher determines his/her optimal effort level, e, so as to maximize
the expected utility:(1)∞

e* = arg max ∫ u (W ) g (W )dW − CS (e),
{e }

−∞

where u(•) represents a teacher’s concave utility function. W is a wage rate and g(•) is a probability
distribution function of W. In other words, g(•) denotes the wage offer function. According to empirical
dicta, the cost created by effort, CS, will be a concave function of effort level, e [Lazear (1996)].
In El Salvador, specific government regulations (Escalafon Magisterial) insure a teacher's job
stability and relatively fixed amount of real wage rate in traditional public schools [World Bank (1995)].
Hence, teacher salaries are not linked directly to the teaching performance in traditional school and can be
described by a fixed wage system. On the other hand, teachers in EDUCO schools receive contingent
payments depending on their teaching performance. The ACEs supervise and evaluate EDUCO teachers’

the empirical literature has focused on identifying the relationship between compensation schemes and proxies ofmeasures of agent’s performance. The most celebrated example of empirically estimating compensation schemes has
been a series of papers that estimate pay-for-performance for executives and, particularly, chief executive officers[for example, Jensen and Murphy (1990)]. However, there is only limited evidence that show that the optimal slopeof the compensation scheme is determined by risk aversion and the returns to effort and contracts are designed to
optimally trade off risk against incentives [Prendergast (1999)]. Yet, it is notable that Kawasaki and McMillan(1987) found supportive evidence of the incentive intensity principle in the Japanese automobile industry.
6
The model constructed in this paper follows a standard principal-agent model with a linear compensation scheme.
For example, see Ross (1973), Hart and Holmstrom (1987), Milgrom and Roberts (1992), and Lazear (1996).
9

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quality and performance. Moreover, EDUCO teachers' job stability itself links to their performance[World Bank (1995)]. However, the true level of teacher effort is not observable to MINED or parental
associations. Hence, we can plausibly represent that a teacher, particularly an EDUCO school teacher, ispaid on the basis of the "observed" level of teaching effort OE, where OE represents the principal's“estimate” of the “true” level of teacher effort. The wage payment scheme is assumed to be a linear
compensation scheme. Then we can represent the teacher payment scheme by a linear function of theobserved level of effort as follows:

W = a1 + a2 OE.

(2)Note that a case of a1 >0 and a2 = 0 represents a fixed wage contract, while a2 > 0 denotes a piece rate
contract.
While we can simply interpret the linearity with respect to effort level as an approximation of ageneral non-linear wage payment formula, theoretically, the linear compensation scheme is shown to bequite robust to the specification of the environment [Holmstrom and Milgrom (1987); Laffont and Tirole(1987); McAfee and McMillan (1987); Hart and Holmstrom (1987)]. The linear payment system ispreferable in the sense that this system applies a uniform incentive pressure that makes the teacher wantto make additional effort no matter what his/her performance has been. Because incremental
improvements in child ability are typically equally desirable for parents after either a slow or a fast start,
this uniform incentive pressure is adequate and optimal. Moreover, linear systems have the advantage ofbeing simple to understand and administer. A complex non-linear scheme might not be able to provide
the desired level of a teacher’s motivation since teachers may not understand the complex scheme easily
and/or a principal cannot administer it as intended.

3.2 The Optimal Teacher Effort

As we have mentioned, the principal will have difficulty observing the level of the true teacher
effort directly. Instead, the principal can observe an imperfect indicator of the true teacher effort, OE,
that is, an indicator that provides some information about the teacher effort but is contaminated byrandom events beyond the control of the teacher. In other words, this observed level of effort is an
estimate of the true level of teacher effort e. Such an indicator may include days of teacher attendance or
intensity of a teacher’s meeting with parents. Formally, we can write

10William Davidson Institute Working Paper Number 307

OE = e + z,

(3)

where z indicates a measurement error with E(z) = 0. Note that e and z are not separately observed by theprincipal. Yet, a more involved community association can observe teacher effort with fewer mistakes
through close and frequent monitoring of a teacher’s behavior. Therefore, the degree of community
participation is naturally regarded as the determinant of precision of teacher effort estimation.
Mathematically, this consideration can be represented as: Var(z) = V(CP), where CP is the degree ofcommunity participation with ∂V/∂CP < 0. Note that the precision of effort estimation is defined as theinverse of the variance of measurement error. This function V(CP) denotes the technical relationshipbetween teacher monitoring by community participation and precision of observing teacher’s true effort.
From (2) and (3), we have the reduced form payment scheme equation:

W = a1 + a2 e + a2 z,(2')

and therefore the teacher optimization problem becomes(4)

e* = argmax{e} E [u(a1 + a2 e + a2 z)] - CS(e).

Assume that a teacher is risk averse with the coefficient of absolute risk aversion, γ. Then a teacher's
certainty equivalent utility is represented approximately by E(W) - CS(e) - (1/2)γVar (W) =E(a1+a2e+a2z) - CS(e) - (1/2)γ(a2)2V(CP). We have the first order condition of a teacher’s utility
maximization problem as follows:(5)a2 = CS'(e*).

This condition is the so-called incentive compatibility constraint. This condition must be satisfied by any
feasible employment constraint. Interpretation of equation (5) is straightforward: a teacher will selecttheir effort levels so that their marginal reward in the left hand side equals their marginal costs fromadditional effort in the right hand side. Hence, we have the optimal level of effort as a function of thepayment scheme parameter:(5’)

e* = e (a2),11William Davidson Institute Working Paper Number 307

where it is easily verified that ∂e*/∂a2 > 0. Using equation (5’), we can discuss the teaching performancein traditional schools. The lack of compensation mechanisms for improved teaching quality and of
adequate supervision restricts the effective control of teacher effort in traditional schools. This
inappropriate incentive structure for teacher payments seems to be responsible for poor teacher
performance and for prevalent teacher absenteeism in traditional schools.

3.3 Optimal Teacher Payment Scheme given by a Parental Association

In the EDUCO schools, the ACE is responsible for hiring and replacing teachers and allocation ofschool budgets. In other words, the ACE has a central role in personnel and budget management and thus
can change the reward structure easily. On the other hand, the SdPF in traditional schools has only alimited role in school management. The payment scheme parameters, a1 and a2, in El Salvadorianschools, are likely to be related systematically with school type and/or the degree of community
participation. The higher intensity of community participation, CP, will be reflected in the teacher rewardand punishment structure. As a result, the optimal level of teacher effort will be higher in EDUCO
schools than in traditional schools. The model in this sub-section will formalize this argument.
If a parental association has full administrative and management ability, the coefficient a2 isdetermined endogenously by the parental association in order to “discipline” teachers.7 The parental
association will concern the social benefit of education, which is measured by a weighted average ofstudent i’s educational achievement, Yi, and a teacher’s benefit. Let λi be the weight for child i, which
represents the teacher and/or parental association’s preference over students. Then, assuming a risk
neutral principal, we can derive the principal’s certain equivalent as E(ΣiλiYi) – E(W). Recall that a risk
avert teacher’s certain equivalent is represented by E(W) – CS(e) – (1/2)γ(a2)2V(CP). Combining these
two formulas, the sum of the certain equivalent incomes of the teacher and the parental association
becomes E(ΣiλiYi) – CS(e) – (1/2)γ(a2)2V(CP). The optimization problem of parental association,
therefore, becomes

(6)

N
1
Max ∑ λi Yi − CS (e) − γ (a 2 ) 2 V (CP ){ a1 , a 2 }2
i =1

s.t. a2 = CS’(e)7

In fact, our data set (the association member questionnaire) indicates that 80% of ACEs in EDUCO schools
discussed about teacher discipline at their meetings.
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William Davidson Institute Working Paper Number 307

where the constraint is the incentive compatibility constraint.8 The first-order condition for this problem
is [Σiλi(∂Y/∂e) - CS'(e) - γ a2 CS"(e)V(CP)](∂e/∂a2) = 0. Hence, the optimal slope of the wage
compensation scheme becomes:N(7)

a 2 * = ∑ λi
i =1

∂Yi
1
•
∂e 1 + γV (CP )CS " (e)This condition, a.k.a., the optimal intensity of incentives condition, indicates that the parental association
will choose a2 optimally to induce the teacher to set the marginal cost of effort equal to its marginal social
value of effort, i.e., weighted average of students educational attainments. We can plausibly treat theeffect of teacher effort on educational output, ∂Y/∂e, as an exogenously fixed technological relationship.
Moreover, to simplify the argument, the curvature of the cost function is approximated so that C"(e) isconstant. Then, this optimal intensity of incentives simply becomes a function of the degree ofcommunity participation and school type parameter:(7’)

a2* = g (CP),where g(CP) ≡ Σλ(∂Y/∂e) / [1 + γV(CP)C"(e)] and it is easily verified that ∂a2*/∂CP > 0. Therefore, wehave a2 as a function of the degree of community participation.
This indicates the important role of community monitoring. The appropriate intensity of an
incentive is affected by the precision with which teacher performance or effort is measured by theprincipal. Under the centralized system, community participation is minimal and thus precision of effort
estimation is quite low. It is unfruitful to use a wage incentive scheme when effort measure is highly
imprecise. As a result, the fixed teacher compensation scheme in traditional schools can be rationalized.
On the other hand, in EDUCO schools, intensive community participation improves measurement ofteacher effort. Strong incentives, therefore, are likely to be optimal when a teacher’s performance is easy
to identify. It is worth noting that the base payment a1 does not enter as a component of the efficiency
consideration of the contract. The fixed base payments, a1, will be determined by an agreement withwhich both parties will obtain the sufficiently high level of welfare.
As mentioned above, specific regulations in traditional schools (Escalafon Magisterial) break alink of teacher salaries to the teaching performance by imposing fixed wage scheme. The typical8Note that this problem is equivalent to the problem of a principal’s total net output maximization subject to anagent’s individual rationality constraint and incentive conpatibility constraint.
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William Davidson Institute Working Paper Number 307

compensation scheme in traditional schools may be described by the case of a1 > 0 and a2 = 0. On theother hand, teachers in EDUCO schools are selected, hired, supervised and dismissed by the parent
associations (ACEs), who judge the quality of EDUCO teachers by their teaching performance. The
typical compensation scheme in EDUCO schools, therefore, becomes a1 ≥ 0 and a2 > 0.
Finally, combining equation (5’) with equation (7’), we have the optimal level of effort as afunction of the level of community participation and school type:e* = e [g (CP)],(8)
where ∂e*/∂CP > 0.

3.4 Moral Hazard

The formal model in this section indicates that the intensity of the incentives provided to teachers
differs according to the degree of community involvement in teacher monitoring. In a typical case, CPED
> CPTR, where CPED and CPTR represent the level of community participation for an EDUCO school and a
traditional school, respectively. As a result, we have a2(CPED) > a2(CPTR) and thus e (CPED) > e (CPTR),
utilizing equations (5) and (5’). These results indicate that the level of teacher effort is systematicallyhigher in EDUCO schools than in traditional schools due to difference in the intensity of community
participation. The positive gap between the teacher effort level in EDUCO schools and that in traditional
schools, i.e., e (CPED) - e (CPTR) > 0, indicates the existence of moral hazard of teacher effort intraditional schools. Since teacher effort itself contributes students’ achievements positively, the possible
relative deficiency of student performance in traditional schools arises from the moral hazard problem ofunobserved teacher effort.

3.5 The Production Function Approach

As we have seen above, the production of educational outcomes is a complex interaction of the
behaviors of various agents who participate in the schooling process such as students, groups of parents,
teachers, and administrators at various levels, from the school up to the education ministry. Moreover,agents not directly connected to the educational system may affect these outcomes if they influence theenvironment in which students learn. For example, the performance of students at schools in a particularcommunity has been attributed to community and peer group effects of social capital, i.e., stock of mutual
14William Davidson Institute Working Paper Number 307

trust [Coleman (1990)]9. To investigate empirically these various aspects of the schooling process in thissection, we employ a conventional reduced form model of educational production [Hanushek (1995)].
Most studies measure output by students' achievement scores, school attendance rates, repetition rates,
school continuation or dropout rates. These variables are thought to capture prospects of future earnings
in the labor market. In this paper, we focus on student scores on standardized achievement tests.
Suppose that a child i's educational achievement, Yi, who is studying at a school j in munitipality
m can be represented by the production function:10

Yi = f (Xi, Cm, Dj, Zj, ej*),(9)

where X represents a vector of student and household characteristics, C is a vector of municipality m's
specific variables, D is an indicator variable of school type attended by a student, where D = 1 for an
EDUCO school and D = 0 for a traditional school, and Z denotes a vector of observed school-level
characteristics which varies by school rather than by student. Equation (9) really expresses achievement
for the ith student in a particular school. To simplify notation, we drop the school subscript. By
combining equation (8) and (9), we can derive a reduced form model as follows:

Yi = f [Xi, Cm, Dj, Zj, e(CPj)].

(10)

This formulation represents that the effect of community participation on outcomes may be mediatedthrough teacher's effort level.11
The optimization behavior of a school administration entity, either MINED in traditional schools
or ACEs in EDUCO schools, leads to the resulting input demand functions:(11)

Z = Z(Cm, Dj),

where a vector of relative prices of various school inputs is represented by municipality-specific variables
C. The school type indicator variable is incorporated as the argument of this demand function since thisvariable potentially affects productivity of school output. Substituting equation (11) into (10), we have a
9

Recent development literature suggests that importance of trust and relationship among people in developing
countries. Fafchamps and Minten (1999), for example, documents the important role that personal relationships playin economic exchange among agricultural traders in Madagascar.
10
Gaynor and Pauly (1990) called this function the “efficient” or “maximum (observed) effort” production function.
11
This function corresponds to the “behavioral production function” defined by Gayor and Pauly (1990) in adifferent context.
15

William Davidson Institute Working Paper Number 307

highly reduced form production function:

Yi = f [Xi, Cm, Dj, Z(Cm, Dj), e(CPj)].

(12)

This reduced form model assumes that the effects on achievement of a school’s observed characteristics,
such as class size, teacher characteristics, etc., are fully determined by its management structure, i.e.,
whether it is EDUCO or not, the characteristics of the students and their parents, and the intensity ofcommunity participation in school administration and management.

4 Data and Empirical Analysis

Our empirical analysis is composed of four parts. First, based on equations (10) and (11), we
employ the “augmented” reduced form educational production function by incorporating parents andcommunity involvement as a major organizational input. Second, we estimate teacher wage compensation
function, utilizing the linear compensation scheme function (2’). Then, the resulting teacher effort functions,(5’) is investigated empirically. In other words, we examine moral hazard of a teacher’s behavior
statistically, by utilizing the theoretical implications of a principal-agent framework. Finally, we estimate
the school-level input demand functions (11). Before presenting econometric framework and empiricalresults in detail, the next sub-section explains the data set used in this paper.

4.1 Data

The data of 311 primary schools, which are randomly sampled from the 3634 primary schools,
was collected in October 1996 by MINED of El Salvador with the assistance of the World Bank and
USAID. The survey covered 162 municipalities out of 262 that share responsibility with the central
government for the delivery of social services. The original data set contains information from four
different school types, pure EDUCO, mixed, traditional public and private schools. Since EDUCO wasintroduced only in 1991, it was not possible to give achievement tests in 1996 to those students who wereabout to finish primary education in EDUCO schools and to compare their scores with those in traditional
schools. Instead, third-grade sections were selected for each school and then five students were randomly
selected in that same section.
The sampling scheme is designed so that the survey is nationally representative. Moreover, theoriginal sample was selected in such a way as to allow for four types of schools -- pure EDUCO, pure16

William Davidson Institute Working Paper Number 307

traditional, mixed, and private schools -- to be considered. In this study, we dropped students fromprivate schools and traditional public urban schools from the sample since their students are notcomparable with the EDUCO students. Moreover, since mixed schools have their own specific school
administration and management structure, we only compare students in the pure EDUCO and puretraditional schools in this paper. This left us with 594 students in 30 pure EDUCO schools and 99 puretraditional rural schools in 88 municipalities. The actual data collection process was composed ofinterviews with five different types of participants in the education community: a director of a sampledschool, a teacher of a sampled 3rd grade class, five randomly-sampled students in the 3rd grade, parents ofthe student, and parents in the parent group of the sampled school. With respect to our notation, thesubscript j = 1,⋅⋅⋅,129 stands for teacher, school, or community (parent) group, and i = 1,⋅⋅⋅,594 represents
students, and m = 1,⋅⋅⋅,88 is for administrative division, either municipalities or department.

Variables and Descriptive Statistics

For dependent variables, the achievement tests for various subjects were applied by MINED on
October 1996 with the assistance of the Intercultural Center for Research in Education (MINED 1997).
These were applied nationally in the 3rd, 4th and 6th grades, but because EDUCO students had reachedonly the 3rd grade at the time of the data collection, we use only the third-grade results in the analysis.
Moreover, we focus only on the results for the mathematics and language tests which are employed
conventionally as measures of educational outcome.
The mathematics test is composed of 30 questions on ten key subjects, that is, three items foreach subject. A student has achieved an objective if she/he got two questions right out of three questions.
For the language test, there are 36 questions on nine objectives, that is, four items each. A student has
achieved an objective if she/he got three questions right out of four questions. According to Table 1, forthis sample, the average student was able to master 3.66 out of 10 subjects in math, but only 1.69 out of 9
in languages. These results are not out of line when compared to national averages (MINED 1997). Of
greater interest, though, are the comparative average values for EDUCO and traditional schools. Students
in EDUCO schools score marginally lower than their traditional school counterparts in both subjects,
although the differences are not statistically significant (Table 1).
Table 1 also lists descriptive statistics of the other variables used in the analysis. In terms ofchild specific characteristics, students are divided equally by gender. A significant portion of them live
without parents, with a slightly higher proportion among EDUCO students. EDUCO students also have aslightly higher number of siblings and are slightly older, although the differences are not significant.
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William Davidson Institute Working Paper Number 307

Using the descriptive statistics in Table 1, we can informally evaluate the three main objectives of
the EDUCO program. First, EDUCO households are poorer than traditional households and parent's
socioeconomic characteristics are much better for traditional school students than for EDUCO students.
Parents of traditional school students have more education than those of EDUCO students. 53% ofmothers or female guardians of traditional students have basic education, compared to 50% for EDUCO
students. The same is true of fathers. The education differences are reflected also in the asset indicators.
Fewer EDUCO parents have access to home-ownership, electricity, sanitary services and running water.
These all suggest that EDUCO students come from poorer background than traditional school students.
For the coverage issue, therefore, EDUCO program has been successful in targeting poorer segments ofthe population.
The socioeconomic characteristics of students and households are consistent with the pattern forschool characteristics. While school level teacher-pupil ratios and the availability of sanitary facilities are
similar in both types, fewer EDUCO schools have access to electricity or piped water. On the other hand,more EDUCO teachers have finished university education but are less experienced. The EDUCO
teaching corps consists of relatively young recent graduates who receive a “bonus” for teaching in the
program. There are no differences in access to textbooks in the two types of schools.
Second, with respect to parent participation issues, parents of EDUCO students participate morein school affairs. Parent associations in EDUCO schools visit classrooms almost 4-5 times more often
than their traditional counterparts. ACEs in EDUCO schools are more involved than are parents’associations in traditional schools. At the parental association meetings, 80% and 79% of ACEs
discussed teacher discipline and attendance of school personnel, respectively, while corresponding figuresof PdSF are 62% and 38%, respectively. Less teacher absenteeism in EDUCO schools might reflect theeffectiveness of potential teacher monitoring by parental associations in these schools. In fact, teachers
are absent from EDUCO schools for 1.09 days per month on average, while 1.35 school days per month
are missed by teachers in traditional schools.
Finally, with respect to the output quality, there are no significant test score differences in spite of
the poorer background of EDUCO students. With a simple t-test on the equality of two means, we cannot
reject the null hypothesis that both schools have the same mean test score. This implies an advantage forcommunity participation in education. In the following sections, we will empirically evaluate whether
there is an EDUCO effect, after controlling for household background and school and classroom inputs,
using a formal econometric framework. By doing this, these conjectures regarding the consequence ofparents’ participation and possible EDUCO effects could be investigated properly.

18William Davidson Institute Working Paper Number 307

4.2 Production Function Estimation

First of all, we estimate a linear approximated version of the reduced form educational production
function of equation (12):Yi = Xiβ+ Cmγ+ Djα + CPjδ + vi,(13)

where u represents a well-behaved measurement error term with assumptions of E(vi)=0 and Var(vi)=σv2.
To control for observed school inputs, we add school characteristics to correspond to the empiricalversions of equation (10).

Yi = Xiβ + Cmγ + Djα + Zjµ + CPjδ + vi.

(14)

Endogenous community participation

So far, we have assumed that the degree of community participation CP and thus themeasurement of teacher performance, which is represented by the variance V, are determined outside thescope of the model. However, CP will be endogenously determined as well, since any monitoring effort
by a community association is costly because of participants’ opportunity costs. As a result, theobservable teacher effort variable, OE, is likely to be endogenous, too. Therefore a simple OLS
estimation of production function (13) and (14) may not provide consistent estimation of coefficients. To
eliminate the potential bias due to this endogeneity, we include the monetary transfer from government as
one of the identifying instrumental variables. The reason of this choice of instrument is explained asfollows.
To derive the optimal level of teacher monitoring, suppose M(V) denotes the minimum amountthat must be spent on monitoring needed to achieve an error variance of teacher effort observation as low
as V. To improve precision of teacher effort prediction should require some resources. When M isexogenously given by the government, the reduced form optimal participation level becomes a function of
government transfer.

(15)

CP* = CP (M).

When monetary transfer, M, is exogenously given, the optimal level of V is determined optimally, which
19

William Davidson Institute Working Paper Number 307

implicitly solves the optimal level of community participation, CP.12
In our particular setting in El Salvador, the amount of participation cost M is exogenously
determined and provided by government: in the case of EDUCO schools, MINED makes a direct transfer
of funds to ACEs. In this case, we can write a reduced form optimal participation level as a function of
government transfer, i.e., CP* = CP (M). Recalling equation (8), we have(16)

e* = e [CP(M)],i.e., optimal level of effort is a positive function of the government’s monetary transfer to a community
association. This means that monetary transfer to the community is used a device to extract teacher effort
whereas the direct payment to teachers by MINED does not enhance the level of teacher effort.
According to the argument above, it will be quite natural to treat the amount of monetary transferfrom government to parental association, M, as an identifying instrumental variable for the level ofcommunity participation, CP, and the observable teacher effort variable, OE. This selection of an
instrumental variable based on the theory is one of the methodological innovations in this paper.
However, the exogeneity assumption of the geographical allocation of government transfers may
be restrictive, since the government transfers might be a function of a community’s characteristics.
Hence, in addition to this transfer variable, we constructed another identifying instrumental variable,
ACPjm, for a school j in a municipality m, which is defined as follows:(17)∑ CP × ( Number of students) - CP

ACPjm = j∈mjm( Number of students) m - 1

j.

This variable captures the municipality level “net” average propensity of community participation. We
can easily verify that ACP is uncorrelated with the error term by construction but is likely to be correlatedwith the participation variable, CP.
Tables 2 and 3 represent resulting municipality fixed effects estimators of production function(13) and (14) for mathematics and language tests, respectively. Regardless of the specification, i.e., withor without controlling for household characteristics, household assets, school and classroom inputs, andteacher quality, consistently positive EDUCO participation effects are observed. Moreover, these12

Our regression analysis of a community participation variable, CP, on the government’s monetary transfer, M,
indicates that there is a positive, statistically significant, and robust effect of the government transfer on the degree ofcommunity participation. The results, which are not reported in this paper, are available from the author.
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William Davidson Institute Working Paper Number 307

coefficients are statistically significant for all language test specifications and most mathematics
specifications. However, the loss of statistical significance when we include classroom quality variables
for mathematics tests (specifications 4 and 8) indicate that positive EDUCO participation effects may bepartly transmitted through better classroom environment in EDUCO schools such as the availability ofclassroom library (Table 2).
With this highly reduced form of a production function approach, however, the transmission
mechanism from community participation to output improvement is not necessarily clear. To investigate
community participation effects, we estimated wage compensation equations, teacher effort functions andinput demand functions in the following section.

4.3 Estimating the compensation scheme

We represent the teacher payment scheme by a linear function of the observed level of effort
multiplied by exponential wage adjustment term. Combining the reduced form teacher payment schemeof equation (2) with optimal wage payment scheme function (7’), we have:(18)

Wj = a1 + g (CPj) OEj.

Note that the sensitivity of observed effort, OE, is represented by g(CP) ≡ Σλ(∂Y/∂e)/[1 + γV(CP)C"(e)].
For estimation purpose, we approximate this equation (18) as follows:(18’)

Wj = a1k + (a20 + a21 CPj) OE + εm + uj,

where u represents a measurement error for econometrician and we assume E(u)=0. This is an estimable
optimal payment scheme function. The first term a1k is a dummy variable which is specific to a teacher’s
grade, k, certified by MINED. This reflects the fact that the amount of the teacher’s fixed payment isbased on the teacher’s formal ranking. In order to control for region-specific unobserved heterogeneity,
department dummy variables, εm, are added.13
We can test whether the intensity of community participation is related to the optimal payment
scheme parameter a2 by testing a null hypothesis of H0: a21 = 0. If we reject the null hypotheses and we
13

Departments are the next lower administrative division in El Salvador after the national level and are higher
division than municipalities. We could employ municipality fixed effects estimation. However, the data unit here isclassroom-level, not student-level, and basic school sampling is based on municipality. In order to guarantee
sufficient degree of freedom, we used department level fixed effects.
21William Davidson Institute Working Paper Number 307

find a statistically significant coefficient on the slope of the payment scheme parameter, i.e., a21 > 0, thenwe may conclude that the finding is consistent with the optimally imposed payment scheme by theprincipal through the community participation.
The remaining empirical issue is how to quantify the observed measure of teacher effort, which is
not observed by the econometrician. Descriptive analysis of our data set shows that almost all EDUCO
school directors, 96.9% of EDUCO school directors, regard that ACEs organize school management throughcontrolling performance and absenteeism of teachers. Moreover, according to the results of the parental
member questionnaire, 80% and 79% of ACEs at EDUCO schools discussed at the group meeting teacher
discipline and attendance of school personnel, respectively. On the other hand, only 62% and 38% of PdSF
in traditional schools discussed these issues. These figures imply that teacher attendance rate might bereflected in ACEs’ determining the teacher compensation in EDUCO schools. Moreover, according to theteacher questionnaire, when a student was absent, 75% of the EDUCO teachers visited the family, while
only 41% of the traditional school teachers visited the family. Hence, observed effort level, OE, is assumed
to be captured by days of teacher’s attendance and hours of teacher-parents meetings. These effort
measures are directly observable to both teacher and parents, and thus teachers might be interested in
improving this measure.14 We will judge the relative importance of these variables as a teacher effort
measure by using various statistical criteria. We will employ two criteria of selection of regressors, i.e.,
the Amemiya’s prediction criterion and the Akaike’s information criterion [Amemiya (1980)].
Estimation results of equation (18) are represented in Table 4. The dependent variable is hourlybase salary of teachers. The first observable effort variable is teacher’s days of school attendance permonth. The second effort variable is teacher's hours per month meeting with parents. The estimatedcoefficients on the interaction variables, a2OE and a2OE2, are all positive. Moreover, these coefficientsare statistically significant except the instrumental variable estimation using teacher’s hours of meetingwith parents variable as the effort variable. Yet, the goodness of fit for these two instrumental variables
results for the hours of meeting variable is relatively poor, according to the Amemiya’s prediction
criterion and the Akaike’s information criterion. Hence, we may conclude that overall results in Table 4
indicate that the slope of wage compensation scheme is positively related with the degree of community
participation, i.e., a21> 0. This finding is consistent with the theoretical prediction of the optimal intensityof incentives condition, a2* = g(CP) with ∂a2*/∂CP > 0. Therefore, EDUCO schools with a relatively
high degree of community participation follow a piece rate payment, which is a compensation scheme
based on the level of observable effort. On the other hand, traditional schools with low level of CP might
have a flatter or even a fixed wage payment scheme.
14

Other candidates of measurable teacher effort include number of dropouts after the initial enrollment (pr_e11),22

William Davidson Institute Working Paper Number 307

Since the Mincer wage equation is a standard empirical formula of wage determination, we also
assume that the estimation model of a wage payment scheme is a combination of linear compensation
scheme and standard Mincer wage equation variables. Modification of equation (18) gives:(19)

Wj = a1k + g (CPj) OEj + QLjβ,

where the wage adjustment term a la Mincer, QL, represents a vector of teacher quality variables such as
education level and length of teaching experience. The linear econometric model of equation (19) is:(19’)

Wj = a1k + (a20 + a21 CPj) OEj + QLjβ + εm + uj.

Estimated coefficients on the teacher quality variables QL are all statistically insignificant (Table 5,
specification II). Moreover, the null hypothesis of all zero β coefficients cannot be rejected for both
effort measures, according to the F-test results reported in Table 5. This indicates that Mincer wage
equation does not fit well for the teacher compensation scheme in El Salvador.

4.4 Estimation Results of Teacher Effort Function

The above estimation results indicate that EDUCO schools teachers receive a piece rate,depending on their performance observed by parental groups. In contrast, traditional schools employ afixed teacher wage scheme. According to equation (5’), the piece rate payment scheme in EDUCO schools
is thought to enhance teacher effort. On the other hand, lack of compensation scheme for improved
teaching quality seems to restrict improved teacher performance in traditional schools. Moreover, choiceof payment scheme should be related with the intensity of community participation according to the
optimal intensity of incentives condition [equation (7)]. Hence, the observed level of teacher effort must
be a function of community participation level [equation (8)].
In order to investigate this mechanism empirically, a linearized version of the teacher effort
function is estimated with both teacher based and household based data sets. From equations (3) and (8),we have OE = e*(CP) + z. By linearizing this, we get an estimation model of the observed effort level:(20)

OEj = α0 + α1Dj + α2CPj + zj.

number of subjects taught (pr_f11), and duration of daily teaching (pr_f12).
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However, we should note that CP and z are likely to be correlated each other by construction. We
therefore employ the instrumental variable method to ensure consistency of estimators too.
There are two different proxy variables of the true effort level used: teacher’s days of school
attendance and teacher’s hours meeting with parents. The estimated coefficients of the effort function
with department dummy variables are presented in Table 5. 15 The estimated coefficient, α2, isconsistently positive with a sufficient level of statistical significance. This finding indicates that EDUCO
school teachers have consistently higher effort level, since parent associations are more able to extract
teacher effort through participation. This implies that the incentive structure and thus the optimal effort
level will be significantly different between EDUCO and traditional schools. We conclude that, with thehigher degree of community participation, the teacher effort measure in EDUCO schools is consistently
better than that in traditional schools. Community participation seems to affect positively the teacher
effort level and thereby creates better performance. This is not a surprising result, given the different wage
payment scheme employed among two types of schools.

4.5 Estimation of school level input demand functions

As we conjectured with production function estimation results, positive EDUCO participation
effects may be partly transmitted through a better classroom environment in EDUCO schools. To
investigate the difference in classroom quality, we estimate the classroom-level school input demand
function. For tractability, a linear function is utilized here:(21)

Zj = π0 + π1Dj + π2Cm + ηj,where we assume that prices are captured by a department dummy variable C.16 We estimated the input
demand functions for classroom variables, since these seem to capture EDUCO effects from the results ofeducational production functions. School type indicator variables are incorporated as the argument of thisdemand function since this potentially affects productivity of school output. As a second specification,
the community participation variable, CP, is also added.
The estimation results are summarized in Table 6. First, we should notice that the number ofstudents in classroom is consistently and significantly lower in EDUCO schools than in traditional schools.
The difference between the average number of students in a traditional school and an EDUCO school15
16

See footnote 13.
See footnote 13. Particularly, this is a useful assumption for maximizing the conditional likelihood function for24

William Davidson Institute Working Paper Number 307

classroom is about seven students. Moreover, the result with the community participation variable indicatesthat the degree of community participation decreases teacher-student ratio significantly. According to theresults reported in Table 2 and 3, a smaller classroom size might improve student achievement, implying thatclassroom size reduction is an important transmission mechanism of community participation. The
coefficients of classroom size, however, are not statistically significant in Table 2 and 3.
Second, the number of books in classroom library is consistently larger in EDUCO schools than thatin traditional schools. Although the coefficients themselves are not statistically significant, on average,
EDUCO schools have fifty to sixty additional books in the classroom library (Table 6). Together with thesignificantly positive coefficients of number of books in the classroom library in the student test scoreregressions (Table 2 and 3), positive EDUCO effect might be well captured by a promotion of the classroom
library of EDUCO schools. This observation of the classroom library effects is consistent with the World
Bank's past evaluation result of the EDUCO program (World Bank 1995, pp.19-20). There may be apositive impact of classroom libraries in completing teacher strategies and in stimulating student interests
and reading habits which affect achievement scores significantly.
Thirdly, textbook availability seems to be better in EDUCO schools, but the coefficients are notstatistically significant. The textbook availability – student achievement nexus also is not clear since thecoefficients of text availability in Table 4 and 5 are not statistically significant. Finally, Table 6 indicatesthat the multigrade classroom is one of distinct characteristics in EDUCO schools. The effect of community
participation on educational output through introduction of multigrade education system is positive, but notstrong since, in Table 2 and 3, the coefficients of the multigrade classroom are not statistically significant.

5 Conclusions

The results of reduced form production functions indicate that, controlling for household
characteristics, household assets, school and teacher quality, we observe consistently positive andstatistically significant EDUCO participation effects. These positive EDUCO effects, however, might be
partly captured by better classroom environment in EDUCO schools such as classroom size and theavailability of a classroom library.
With this reduced form production function approach, however, the transmissions structure from
community participation to output improvement is not necessarily clear. To investigate the structural
relationship between community participation and educational outputs, we estimated teacher compensation
functions, teacher effort functions and input demand functions. First, teacher wage payment is relatively
logit model with fixed effects.
25William Davidson Institute Working Paper Number 307

fixed in traditional schools, while EDUCO school teachers receive piece rate, depending on theirperformance. We should also note that the slope of wage equation is positively affected by the degree ofcommunity participation. This finding can be interpreted as the optimal intensity of community
participation. Second, teacher effort measure in EDUCO schools with the high intensity of community
participation is consistently better than that in traditional schools. Community participation seems toenhance the teacher effort level through creating an appropriate design for the teacher’s wage
compensation scheme. ACE visits increase academic performance indirectly. On the other hand, poorer
student performance in traditional schools arises from moral hazard problem of unobservable teacher effort.
Our interpretation of these findings is admittedly not the only possible interpretation, but is the one that
seems most natural.
We also found that community participation increases educational productivity through improving
schools inputs. First, the degree of community participation decreases teacher-student ratio or classroom
sizes significantly. Together with the production function estimation results which show that a smallerclassroom size improves student achievement, classroom size reduction maybe an important transmission
mechanism of community participation. Second, the number of books in classroom library is consistently
larger in EDUCO schools than that in traditional schools, and we found that positive and significant
coefficients of the number of books in the classroom libraries reflected in the student test score regressions.
Positive EDUCO effect might be well captured by the positive effects of larger classroom library size on
student performance,
The most important contribution of this paper is applications of the agency framework to social
sector management issues, together with empirical estimations of theoretical implications. With respect
to the estimation methodology, the paper has three contributions. First, in order to select the observed
measure of an agent’s effort level, the empirical framework employed the econometric approach of theselection of regressor, combined with descriptive statistics and qualitative information of the parental
association questionnaire. Second, this paper empirically investigates the consistency between theobserved contracts, and the theoretical prediction of the model, the consistency which is still missing in
the literature. Third, with respect to the econometric framework, this paper has a methodologicalinnovation in the selection of identifying instrumental variables for the community participation variable,
which is endogenously determined. Based on the theoretical condition of the monitoring intensity
principle, we select the amount of monetary transfer from the government to the community associations
as an identifying instrumental variable.
Recalling equation (16), the community participation is likely to be enhanced by the government's
support. Indeed, we found that there is a positive and statistically significant effect of the government’s

26

William Davidson Institute Working Paper Number 307

monetary transfer to the community group on the degree of community participation, the result which is notreported in this paper.17 Hence, we can plausibly suppose that the level of teacher effort is a positive
function of the government’s monetary transfer to the community association. This means that monetarytransfer to the community is used as a teacher effort extraction device, whereas the direct payment toteachers by MINED does not enhance the level of teacher effort sufficiently. Our empirical results
indicate that decentralization of an education system, which involves delegation of school administrationand teacher management to a community, results in substantial gains in school productivity.
Our empirical results support the view that participation is necessary for a fully effective social
development. Community participation not only utilizes larger sets of relevant information in order toovercome the information problems, but also brings commitment and thus greater effort of agents.
Moreover, local-level participation in project implementation ensures consistency between a policy andlocal demand. Community participation in school management improves educational quality since
educational resources are allocated according to local conditions and thus their allocation becomes
responsive to parents and communities. Yet, Hanushek (1994) emphasizes that“the advantages of local decision-making materialize only when local decision-makers have sufficient
information, when incentive structure emphasizes performance toward agreed-upon goals, and when there
is considerable latitude for making local decisions, and when there is a good system of accountability.
Most of school-based management schemes do not meet these conditions; indeed many fail on allconditions (p.101).”
The EDUCO experience suggests that parents’ management of the decentralized schools in El Salvador
does meet all these conditions. The findings in this paper should shed important light on future efforts toreform education systems in both developed and developing countries.

17

The results are available from the author.
27

William Davidson Institute Working Paper Number 307

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30

William Davidson Institute Working Paper Number 307

Figure 1
The Augmented Production Function Modelwith Endogenous Teacher Effort(Governance Structure of Traditional Schools)

MINED

Strong effects
No strong effects

Decision Making

School Inputs -- Management -- TEACHER

Schooling
Parents-teacher Meetings

SdPF

OUTPUT

Home Teaching
PARENTS31

William Davidson Institute Working Paper Number 307

Figure 2
The Augmented Production Function Modelwith Endogenous Teacher Effort(Governance Structure of EDUCO Schools)

MINED

School Inputs -- Management -- TEACHER

Input
Decision

Monitoring
by Imposing

Schooling

A Payment Scheme

ACE

Parents-teacher Meetings

Home Teaching
PARENTS32

OUTPUTWilliam Davidson Institute Working Paper Number 307

Table 1
Summary Statistics by School Type
Variable definitions

Code

All Schools

EDUCO

Traditional

Achievement test score, math (# of subjects taken)

ma3mas3.70 (2.55)

3.59 (2.77)

3.74 (2.48)

Achievement test score, language (# of subjects taken)

le3mas1.74 (1.71)

1.73 (1.84)

1.75 (1.66)

Gender (female=1)

a_d_1d

0.51

0.51

0.51

Child’s age

childage

10.59 (1.76)

11.01 (1.97)

10.45 (1.67)

Live without parent(s)=1

a_c_1d2

0.14

0.16

0.13

Cognitive Outcome

Child Generic Chracteristics (CHG)

Household Generic Characteristics (HHG)
Mother enter basic education=1

edl_m

0.53

0.50

0.53

Mother’s education missing=1

ed_mm

0.08

0.06

0.09

Father enter basic education=1

edl_p

0.39

0.38

0.40

Father’s education missing=1

ed_pm

0.04

0.03

0.04

Number of siblings (age of 4-15)

pa_b3

2.01 (1.54)

2.11 (1.50)

1.98 (1.55)

Own house=1

pa_e1d

0.72

0.68

0.73

Electricity available=1

pa_e81d

0.57

0.28

0.66

Household Assets (HHA)

Sanitation available=1

pa_e82d

0.18

0.06

0.21

Water available=1

pa_e85d

0.06

0.01

0.08

School Quality (SQ)
Teaher-pupil ratio (school level)

d_p_all

0.04 (0.06)

0.05 (0.09)

0.03 (0.04)

If sanitation/latrine available at school=1

d_d11d

0.93

0.89

0.94

If electricity available at school=1

d_d12d

0.68

0.30

0.80

If piped water available at school=1

d_d21d

0.31

0.12

0.37

Teacher Quality (TQ)
=1 if teacher is female

pr_fem

0.71

0.65

0.73

=1 if teacher finish University education

predu_un

0.46

0.75

0.37

Year of teacher experience

pr_year7.76 (6.44)

4.37 (2.70)

8.83 (6.89)

pr_age32.53 (7.31)

27.55 (4.02)

34.10 (7.41)# of students in the classroom

pr_d2

26.39 (10.75)

20.75 (6.39)

28.15 (11.23)

If all students have math and/or language textbook=1

pr_text
0.19

Year of teacher experience squared
Age of the teacher
Classroom Quality (CQ)

If textbook data missing=1

text_m=1 if teacher teaches in multigrade classroom

pr_d15d

0.24

0.39

=1 if multigrade information missing

pr_d15m

0.01

0.04

# of books in classroom librarybooks

75.57 (199.37) 114.63 (272.84)

63.29 (168.41)=1 if classroom library information missing

book_m

0.47

0.24

0.54

pr_d112.38 (4.56)

5.65 (6.59)

1.35 (3.05)

Transfer from Government to parental associationtransf

Net average intensity of participation

acp

2317.86(4780.50)
2.09 (1.25)

8325.11(6215.20)
2.31 (1.24)

430.63(1743.60)
2.02 (1.24)

Number of Observations

N

594

142

452

0

Community Participation Variables (CP)# of ACE/SpDF’s visits to classroom per month
Instrumental Variables

Note: Standard errors are in parentheses

33William Davidson Institute Working Paper Number 307

Table 1 (continued)
Definition, means, and standard deviation of variables by school type
Variables used for teacher wage and effort function
Variable definitions

Code

All Schools

EDUCO

Traditional

Monthly salary of teacher

pr_c2

Salary per hour

wage

3025.11(516.44)
32.98(23.40)2914.90(275.35)30.47(17.64)3058.60(566.74)
33.74(24.92)26.92(1.37)
3.41(2.69)

26.92(1.16)
4.94(3.18)

26.62(1.43)
2.94(2.35)

133

31

102

Teacher Wage

Teacher Effort Variables
Teacher’s school attendance per monthattendTeacher’s hours per month meeting withparents

pr_f123

Number of Observations

N

34

William Davidson Institute Working Paper Number 307

Table 2
Production Function Estimation with Municipality Fixed Effects
Dependent Variable: Mathematics Test Score
Sample

spec.

1
OLS

OLS

OLS

OLS

IV

IV

IV

IV

Code

Coef.
t

Coef.
t

Coef.
t

Coef.
t

Coef.
t

Coef.
t

Coef.
t

Coef.
t

pr_d11+0.12(1.75)*-0.91(1.31)

0.12(1.66)*-0.60(0.86)

0.15(2.04)**-0.60(0.82)

Method of estimation
Variable definitions

2

3

4

5

6

7

8

Community Participation
Varibale (CP)# of ACE/SdPF's visits toclassroom per month+=1 if EDUCO schools

e_w

0.12
(1.48)-1.13(1.37)

0.10(1.42)-0.82(1.19)

0.11(1.45)-0.54(0.79)

0.15(1.96)**-0.61(0.83)

0.09(1.08)-1.02(1.26)

Child Generic Chracteristics(CHG)
Gender (female=1)

a_d_1dChild’s age

childageLive without parent(s)=1

a_c_1d2

-0.66
-0.65
-0.61
-0.52
(3.07)*** (2.99)*** (2.80)*** (2.37)**
0.18
0.18
0.17
0.17
(2.86)*** (2.82)*** (2.71)*** (2.65)***
0.41
0.39
0.42
0.47
(1.17)(1.13)(1.20)(1.36)

-0.67
-0.65
-0.61
-0.53
(3.09)*** (3.00)*** (2.79)*** (2.39)**
0.18
0.18
0.17
0.17
(2.88)*** (2.83)*** (2.70)*** (2.66)***
0.41
0.40
0.42
0.48
(1.17)(1.14)(1.20)(1.36)-0.05(0.22)-0.14(0.31)-0.05(0.19)
0.58(0.93)-0.04(0.52)-0.05(0.19)-0.23(0.52)-0.07(0.27)
0.44(0.69)-0.03(0.44)-0.09(0.36)-0.25(0.56)-0.04(0.16)
0.45(0.72)-0.03(0.46)-0.06(0.23)-0.02(0.05)-0.07(0.30)
0.44(0.70)-0.02(0.25)-0.06(0.23)-0.14(0.32)-0.05(0.20)
0.57(0.92)-0.04(0.54)-0.05(0.19)-0.23(0.53)-0.07(0.28)
0.43(0.68)-0.03(0.46)-0.09(0.36)-0.25(0.56)-0.04(0.16)
0.45(0.72)-0.03(0.45)-0.06(0.25)-0.02(0.05)-0.08(0.32)
0.43(0.69)-0.02(0.27)-0.16(0.61)
0.03(0.10)
0.56(1.71)*-0.30(0.64)-0.15(0.57)-0.04(0.12)
0.50(1.49)-0.22(0.46)-0.12(0.44)-0.03(0.08)
0.51(1.52)-0.25(0.52)-0.22(0.84)
0.05(0.15)
0.56(1.65)*-0.20(0.42)-0.16(0.62)
0.03(0.11)
0.57(1.73)*-0.31(0.66)-0.15(0.57)-0.04(0.11)
0.51(1.50)-0.22(0.47)-0.12(0.44)-0.03(0.08)
0.51(1.52)-0.25(0.52)-0.22(0.86)
0.06(0.18)
0.57(1.68)*-0.21(0.44)

Household GenericCharacteristics (HHG)
Mother enter basic
edl_m
education=1
Mother’s education
ed_mm
missing=1
Father enter basic
edl_p
education=1
Father’s education missing=1 ed_pm
Number of siblings (age of 4- pa_b3
15)Household Assets (HHA)
Own house=1

pa_e1dElectricity available=1

pa_e81d

Sanitation available=1

pa_e82d

Water available=1

pa_e85dNote 1) Huber-White consistent robust standard errors are reported. The symbols *, **, and *** indicate statisticalsignificance at 10%, 5%, and 1% levels, respectively.
Note 2) + indicates endogenous variable. Instrumental variables are composed of all exogenous variables,
the government’s monetary transfer (M), and average community participation (ACP).

35

William Davidson Institute Working Paper Number 307

Table 2 (continued)
Production Function Estimation with Municipality Fixed Effects
Dependent Variable: Mathematics Test Score
Samplespec.

Method of estimation
Variable definitions

Code

1

2

3

4

5

6

7

8

OLS

OLS

OLS

OLS

IV

IV

IV

IV

Coef.
t

Coef.
t

Coef.
t

Coef.
t

Coef.
t

Coef.
t

Coef.
t

Coef.
t

-36.70(1.59)
0.51(0.80)
0.36(0.71)-0.15(0.32)-34.19(1.47)
0.72(0.98)
0.04(0.08)-0.28(0.59)-28.70(1.17)
1.39(1.61)
0.55(0.93)
0.06(0.11)-37.14(1.62)*
0.51(0.80)
0.34(0.67)-0.14(0.30)-34.05(1.47)
0.71(0.97)
0.05(0.09)-0.28(0.59)-30.17(1.25)
1.43(1.63)
0.55(0.93)
0.07(0.13)-0.29(0.56)-0.92(1.99)
0.04(0.59)-0.02(0.51)-0.28(0.47)-0.76(1.40)
0.06(0.86)-0.06(1.04)-0.29(0.56)-0.92(1.95)
0.04(0.58)-0.02(0.50)-0.26(0.44)-0.72(1.28)
0.06(0.90)-0.06(1.09)

School Quality (SQ)
Teaher-pupil ratio (schoollevel)
If sanitation/latrine available
at shool=1
If electricity available atschool=1
If piped water available atschool=1
If library available

d_p_alld_d11dd_d12dd_d21dpr_d55d

Teacher Quality (TQ)
=1 if teacher is female

pr_fem=1 if teacher finish
University education
Year of teacher experience

predu_un
pr_yearAge of the teacher

pr_ageClassroom Quality (CQ)
# of students in the classroom pr_d2
If all students have mathand/or lanugage textbook=1
If textbook data
missing=1# of books in classroom
library (unit=100 books)
If # of books data
missing=1=1 if teacher teaches in
multigrade classroom=1 if multigrade information
missing-0.05(1.43)-0.13(0.28)
1.50(1.68)*
0.25(2.45)**
0.60(0.85)
0.14(0.19)
0.27(0.13)

pr_text
text_mbooks
book_mpr_d15dpr_d15m-0.05(1.48)-0.13(0.28)
1.52(1.70)*
0.26(2.47)**
0.63(0.89)
0.07(0.11)
0.47(0.23)

Constant

_cons

2.81
2.83
(3.09)*** (2.00)**

3.44(2.16)**

R-squared

R2

0.28

0.29

0.30

0.32

0.28

0.29

0.30

0.32

Number of Observations

N

594

594

594

594

594

594

594

594

4.31
(2.17)**

2.81
2.85(3.09)*** (2.02)**

3.43(2.18)**

4.43(2.24)**

Note 1) Huber-White consistent robust standard errors are reported. The symbols *, **, and *** indicate statisticalsignificance at 10%, 5%, and 1% levels, respectively.
Note 2) + indicates endogenous variable. Instrumental variables are composed of all exogenous variables,
the government’s monetary transfer (M), and average community participation (ACP).

36

William Davidson Institute Working Paper Number 307

Table 3
Production Function Estimation with Municipality Fixed Effects
Dependent Variable: Language Test Score
Sample

spec.

1
OLS

OLS

OLS

OLS

IV

IV

IV

IV

Variable definitions

Code

Coef.
t

Coef.
t

Coef.
t

Coef.
t

Coef.
t

Coef.
t

Coef.
t

Coef.
t

Community Participation
Varibale (CP)# of ACE/SdPF's visits toclassroom per month+=1 if EDUCO schools

pr_d11+0.10(2.04)**-0.13(0.28)

0.11(2.15)**-0.17(0.34)

0.15
0.12(2.70)*** (2.00)**
-0.35
-0.78
(0.70)(1.41)

0.11(2.21)**-0.19(0.40)

0.12(2.26)**-0.20(0.41)

0.16
0.11(2.82)*** (1.83)*
-0.43
-0.76
(0.85)(1.38)

0.03(0.20)
0.04(0.91)
0.48(1.89)*

0.04(0.25)
0.04(0.86)
0.47(1.87)*

0.05(0.30)
0.04(0.90)
0.49(1.95)*

0.08(0.52)
0.03(0.70)
0.51(2.04)**

0.03(0.22)
0.04(0.90)
0.48(1.88)*

0.04(0.26)
0.04(0.86)
0.47(1.86)*

0.05(0.32)
0.04(0.88)
0.49(1.94)*

0.08(0.51)
0.03(0.70)
0.52(2.04)**

0.06(0.36)
0.23(0.80)
0.15(0.91)-0.51(1.34)-0.01(0.23)

0.06(0.35)
0.26(0.90)
0.15(0.87)-0.52-1.33)-0.01(0.15)

0.06(0.37)
0.19(0.65)
0.13(0.79)-0.49(1.26)-0.02(0.39)

0.07(0.40)
0.31(1.03)
0.08(0.51)-0.46(1.17)-0.02(0.32)

0.06(0.36)
0.23(0.81)
0.15(0.92)-0.51(1.33)-0.01(0.21)

0.06(0.35)
0.26(0.91)
0.15(0.87)-0.52(1.32)-0.01(0.14)

0.06(0.37)
0.20(0.66)
0.13(0.79)-0.48(1.24)-0.02(0.37)

0.07(0.40)
0.31(1.03)
0.08(0.50)-0.46(1.18)-0.02(0.33)

0.02(0.12)-0.02(0.07)
0.30(1.27)-0.45(1.16)

0.01(0.03)-0.05(0.24)
0.28(1.21)-0.47(1.21)

0.01(0.06)-0.08(0.37)
0.30(1.25)-0.45(1.18)-0.07(0.34)-0.01(0.03)
0.32(1.31)-0.44(1.15)

0.02(0.13)-0.02(0.08)
0.29(1.25)-0.44(1.15)

0.01(0.03)-0.05(0.25)
0.28(1.19)-0.46(1.21)

0.01(0.06)-0.08(0.40)
0.29(1.22)-0.44(1.16)-0.07(0.34)-0.01(0.02)
0.32(1.31)-0.44(1.16)

Method of estimation

e_w

2

3

4

5

6

7

8

Child Generic Chracteristics(CHG)
Gender (female=1)

a_d_1dChild’s age

childageLive without parent(s)=1

a_c_1d2

Household GenericCharacteristics (HHG)
Mother enter basic
edl_m
education=1
Mother’s education
ed_mm
missing=1
Father enter basic
edl_p
education=1
Father’s education missing=1 ed_pm
Number of siblings (age of 4- pa_b3
15)Household Assets (HHA)
Own house=1

pa_e1dElectricity available=1

pa_e81d

Sanitation available=1

pa_e82d

Water available=1

pa_e85dNote 1) Huber-White consistent robust standard errors are reported. The symbols *, **, and *** indicate statisticalsignificance at 10%, 5%, and 1% levels, respectively.
Note 2) + indicates endogenous variable. Instrumental variables are composed of all exogenous variables,
government’s monetary transfer (M), and average community participation (ACP).

37

William Davidson Institute Working Paper Number 307

Table 3 (continued)
Production Function Estimation with Municipality Fixed Effects
Dependent Variable: Language Test Score
Samplespec.

Method of estimation
Variable definitions

Code

1

2

3

4

5

6

7

8

OLS

OLS

OLS

OLS

IV

IV

IV

IV

Coef.
t

Coef.
t

Coef.
t

Coef.
t

Coef.
t

Coef.
t

Coef.
t

Coef.
t

School Quality (SQ)
Teaher-pupil ratio (schoollevel)
If sanitation/latrine available
at shool=1
If electricity available atschool=1
If piped water available atschool=1

d_p_all

5.87(0.36)-0.22(0.43)
0.24(0.66)-0.18(0.57)

d_d11dd_d12dd_d21d13.32(0.83)-0.59(1.10)
0.04(0.11)-0.30(0.93)

13.04(0.78)
0.05(0.08)
0.39(0.96)-0.20(0.57)

0.01(0.03)-0.72(2.24)**-0.06(1.46)
0.06(1.92)*-0.05(0.14)-0.62(1.73)*-0.05(1.08)
0.04(1.36)

6.14(0.38)-0.22(0.43)
0.25(0.69)-0.18(0.58)

14.04(0.88)-0.59(1.10)
0.06(0.16)-0.31(0.97)

12.77(0.77)
0.06(0.09)
0.39(0.96)-0.20(0.56)

0.00(0.01)-0.74(2.26)**-0.06(1.51)
0.06(1.96)*-0.05(0.13)-0.61(1.67)*-0.05(1.06)
0.04(1.33)

Teacher Quality (TQ)
=1 if teacher is female

pr_fem=1 if teacher finish
University education
Year of teacher experience

predu_unAge of the teacher

pr_agepr_yearClassroom Quality (CQ)
# of students in the classroom pr_d2
If all students have mathand/or lanugage textbook=1
If textbook data
missing=1# of books in classroom
library (unit=100 books)
If # of books data
missing=1=1 if teacher teaches in
multigrade classroom=1 if multigrade information
missing-0.03(1.53)-0.31(1.10)
0.59(1.02)
0.20(2.41)**
0.10(0.23)
0.19(0.41)
1.65
0.94

pr_text
text_mbooks
book_mpr_d15dpr_d15m-0.03(1.54)-0.31(1.10)
0.59(1.03)
0.20(2.41)**
0.10(0.24)
0.17(0.39)
1.68
0.95

constant

_cons

2.65
2.76
2.08
(4.16)*** (2.74)*** (1.86)*

2.98(2.21)**

2.65
2.75
2.05
(4.17)*** (2.74)*** (1.84)*

3.01(2.26)**

R-squared

R2

0.23

0.24

0.25

0.27

0.23

0.24

0.25

0.27

Number of Observations

N

594

594

594

594

594

594

594

594

Note 1) Huber-White consistent robust standard errors are reported. The symbols *, **, and *** indicate statisticalsignificance at 10%, 5%, and 1% levels, respectively.
Note 2) + indicates endogenous variable. Instrumental variables are composed of all exogenous variables,
government’s monetary transfer (M), and average community participation (ACP).

38

William Davidson Institute Working Paper Number 307

Table 4
Estimation Results of Wage Compensation Scheme
Dependent Variable: Wage Payment Per Hour
Specification
Method of estimation= 1 if EDUCO

e_w

Teacher’s days of attendance per attend+month+
pr_d11*attend+
a2OE2+

I
OLS
Coef.
t

I
IV
Coef.
t

I
OLS
Coef.
t

I
IV
Coef.
t

II
OLS
Coef.
t

II
IV
Coef.
t

II
OLS
Coef.
t

II
IV
Coef.
t

-5.50
(1.30)-2.43(0.96)
0.04(2.12)**-7.84(2.19)**-2.48(0.59)
0.04(2.07)**-7.53(1.66)*-15.84(1.28)-3.18(0.77)-2.89(1.09)
0.04(2.17)**-1.11(0.27)-5.66(1.53)
0.04(2.21)**-5.64(1.04)-7.48(0.88)-0.39(0.53)
0.19(1.96)**

3.65(0.52)
0.06(0.31)-0.48(0.58)
0.17(1.79)*

1.21(0.33)
0.10(0.88)

Teacher's meeting with parents + pr_f123+
pr_d11*pr_f123 +a2OE+

Mincer equation variables= 1 if female teacher

fem

91.99
(1.36)

234.71(2.46)**

29.51
20.57
(4.17)*** (1.04)-2.09(0.37)
7.25(0.79)
0.47(0.05)
1.27(1.16)-0.04(1.17)
91.68(1.24)=1 if finish technical school

predu_tc= 1 if finish collage

predu_unYears of past teachingexperience
Years of past teachingexperience squared
constant

pr_yearNumber of observations
R-squared

133
0.12

133
0.05

133
0.13

133
0.00

133
0.15

133
0.13

133
0.14

133
0.13

RSS (in thousands)

63.43

69.00

62.85

72.30

61.23

62.68

61.66

63.15

Amemiya’s Prediction Criterion(in thousands)
Akaike’s Information Criterion(in thousands)

85.64

93.15

85.10

97.89

89.58

91.70

90.21

92.34

86.89

94.53

86.10

99.05

90.62

92.77

91.25

93.46

0.69
[0.63]

0.81[0.54]

0.30[0.91]

0.31[0.90]

year2
_cons

Test statistics for Mincer wage
equation variables (F-value forthe null hypothesis of all β’s are
zero)

-2.31
(0.39)
7.44(0.80)
0.40(0.04)
1.56(1.24)-0.05(1.31)
163.18(1.68)*-1.33(0.24)
6.75(0.74)
1.41(0.15)
0.77(0.66)-0.02(0.63)
20.01(1.55)-0.56(0.10)
6.48(0.74)
2.61(0.27)
1.03(0.83)-0.03(0.80)
14.95(0.85)

Note 1) Huber-White consistent robust standard errors are reported. The symbols *, **, and *** indicate statisticalsignificance at 10%, 5%, and 1% levels, respectively.
Note 2) + indicates endogenous variable. Instrumental variables are composed of all exogenous variables,
government’s monetary transfer (M), M squared, average community participation (ACP), ACP squared, and M-ACP
interaction variable.

39

William Davidson Institute Working Paper Number 307

Table 5
Estimation of Teacher Effort Function
by Department Fixed Effects Estimation
Dependent variable:
Teacher's days of school attendance or hours meeting with parents (per month)
Dependent variableattendMethod of Estimation

constant_cons=1 if EDUCO

e_w# of ACE/SpDF's visits to classroom permonth+

pr_d11+R-squared
Number of Observation

R2
N

pr_f123

OLS
IV
Coef.
Coef.
t
t
26.25
26.25
(57.31)*** (57.49)***
0.43
0.44
(1.35)(1.39)
0.05
0.05(2.09)** (1.82)*

OLS
Coef.
t
2.74
(2.41)**
1.49(2.81)***
0.15(2.28)**

IV
Coef.
t
1.96
(2.47)**
1.51(2.69)***
0.15(2.13)**

0.23
133

0.26
133

0.26
133

0.23
133

Note 1) Huber-White consistent robust standard errors are reported. The symbols *, **, and ***indicate statistical significance at 10%, 5%, and 1% levels, respectively.
Note 2) + indicates endogenous variable. Instrumental variables are composed of all exogenous
variables, government’s monetary transfer (M), and average community participation (ACP).

40

William Davidson Institute Working Paper Number 307

Table 6
Fixed Effects Estimation of Classroom-level Input Demand Function
Dependent Variable# of students inclassroom (pr_d2)=1 if all students# of books in classroom =1 if multigrade
have math and/or
library
classroom
language textbooks
Within Department
Conditional logit
Within Department
Conditional logit
Estimator with Robust
Estimator with RobustStandard Errors
Standard Errors
1
2
1
2
1
2
1
2
Coef.
Coef.
Coef.
Coef.
Coef.
Coef.
Coef.
Coef.
t
t
z
z
t
t
z
z

Estimation Method

Specfication

constant_cons=1 if EDUCO

e_w

37.78
(8.36)***-7.12(4.33)***

38.16(8.44)***
-6.18
0.64
(3.49)*** (1.01)-0.22(2.30)**

0.95(1.25)-0.06(0.83)

0.18
133

0.18
133

0.02
103

# of ACE/SpDF's
pr_d11
visits to classroom permonth
R-squared
Number of
Observation

R2
N

0.01
103

-3.17
(0.26)
62.69(1.31)-10.39(0.73)
44.71(0.81)
4.26(1.16)

0.80(1.62)

0.62(1.24)
0.07(1.39)

0.11
133

0.11
133

0.02
124

0.04
124

Note 1) The symbols *, **, and *** indicate statistical significance at 10%, 5%, and 1% levels,
respectively
Note 2) Logit model with department fixed effects is estimated consistently by maximizing conditional
likelihood function.

41